4.5 Article

Energy efficient cluster based routing protocol for WSN using butterfly optimization algorithm and ant colony optimization

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AD HOC NETWORKS
卷 110, 期 -, 页码 -

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ELSEVIER
DOI: 10.1016/j.adhoc.2020.102317

关键词

Ant colony optimization; Butterfly optimization algorithm; Energy consumption; Network lifetime; Wireless sensor networks

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The objective of the study is to maximize the network lifetime by minimizing overall energy consumption using Butterfly Optimization Algorithm and Ant Colony Optimization. The proposed methodology outperforms traditional methods in terms of the number of alive nodes and energy consumption.
Wireless Sensor Networks (WSNs) consist of a large number of spatially distributed sensor nodes connected through the wireless medium to monitor and record the physical information from the environment. The nodes of WSN are battery powered, so after a certain period it loose entire energy. This energy constraint affects the lifetime of the network. The objective of this study is to minimize the overall energy consumption and to maximize the network lifetime. At present, clustering and routing algorithms are widely used in WSNs to enhance the network lifetime. In this study, the Butterfly Optimization Algorithm (BOA) is employed to choose an optimal cluster head from a group of nodes. The cluster head selection is optimized by the residual energy of the nodes, distance to the neighbors, distance to the base station, node degree and node centrality. The route between the cluster head and the base station is identified by using Ant Colony Optimization (ACO), it selects the optimal route based on the distance, residual energy and node degree. The performance measures of this proposed methodology are analyzed in terms of alive nodes, dead nodes, energy consumption and data packets received by the BS. The outputs of the proposed methodology are compared with traditional approaches LEACH, DEEC and compared with some existing methods FUCHAR, CRHS, BERA, CPSO, ALOC and FLION. For example, the alive nodes of the proposed methodology are 200 at 1500 iterations which is higher compared to the CRHS and BERA methods.

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